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August
2021, 1(3): 186-198.
doi: 10.3934/steme.2021014
Neural network training in SCILAB for classifying mango (Mangifera indica) according to maturity level using the RGB color model
| 1. | Department of Mechatronics, Instituto Politecnico Nacional, CICATA Unidad Queréro, CP 76090, México |
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Industries that use fruits as raw materials must, at some point in the process, classify them to discard the unsuitable ones and thus ensure the quality of the final product. To produce mango nectar, it is necessary to ensure that the mango is mature enough to start the extraction of the nectar; however, sorting thousands of mangoes may require many people, who can easily lose attention and reduce the accuracy of the result. Such kind of decision can be supported by current Artificial Intelligence techniques. The theoretical details of the processing are presented, as well as the programming code of the neural network using SCILAB as a computer language; the code includes the color extraction from mango images. SCILAB programming is simple, efficient and does not require computers with large processing capacity. The classification was validated with 30 images (TIF format) of Manila variety mango; the mangoes were placed on a blue background to easily separate the background from the object of interest. Four and six mangoes were used to train the neural network. This application of neural networks is part of an undergraduate course on artificial intelligence, which shows the potential of these techniques for solving real and concrete problems.
Citation:
Eduardo Castillo-Castaneda. Neural network training in SCILAB for classifying mango (Mangifera indica) according to maturity level using the RGB color model. STEM Education,
2021, 1
(3)
: 186-198.
doi: 10.3934/steme.2021014
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References:
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S.N. Jha, S. Chopra and A.R.P. Kingsly, Modeling of color values for nondestructive evaluation of maturity of mango, Journal of Food Engineering, 78 (2007), 22-26. Google Scholar |
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D.S. Prabha and J.S. Kumar, Assessment of banana fruit maturity by image processing technique, J Food Sci Technol, 52 (2015), 1216-1327. Google Scholar |
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R. Torres, E.J. Montes, O.A. Pérez and R.D. Andrade, Relación del Color y del Estado de Madurez, con las Propiedades FisicoquȪmica de Frutas Tropicales, Información Tecnológica, 24 (2013), 51-56. Google Scholar |
| [8] | J.C. Russ, The Image Processing Handbook, Prentice Hall, 1999. Google Scholar |
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show all references
References:
| [1] |
S.N. Jha, S. Chopra and A.R.P. Kingsly, Modeling of color values for nondestructive evaluation of maturity of mango, Journal of Food Engineering, 78 (2007), 22-26. Google Scholar |
| [2] |
In: https://www.magmar.com.mx/, available 4th July 2021. Google Scholar |
| [3] | S.J. Russell and P. Norvig, Artificial Intelligence: A modern Approach, Prentice Hall, New Jersey, USA, 1995. Google Scholar |
| [4] |
Bughin, J., Seong, J., Manyika, J., Chui, M. and Joshi, R., Notes from AI frontier: Modeling the impact of AI on the world economy. Discussion paper, 2018, McKinsey Global Institute. Google Scholar |
| [5] | S. Lucci and D. Kopec, Artificial Intelligence in the 21st Century, Mercury Learning and Information, VA, USA, 2015. Google Scholar |
| [6] |
D.S. Prabha and J.S. Kumar, Assessment of banana fruit maturity by image processing technique, J Food Sci Technol, 52 (2015), 1216-1327. Google Scholar |
| [7] |
R. Torres, E.J. Montes, O.A. Pérez and R.D. Andrade, Relación del Color y del Estado de Madurez, con las Propiedades FisicoquȪmica de Frutas Tropicales, Información Tecnológica, 24 (2013), 51-56. Google Scholar |
| [8] | J.C. Russ, The Image Processing Handbook, Prentice Hall, 1999. Google Scholar |
| [9] | R.C. González and R.E. Woods, Digital Image Processing, Prentice Hall, 2002. Google Scholar |
| [10] |
F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain, Psychological Review, 65 (1958). Google Scholar |
| [11] |
Rojas, R., The Backpropagation Algorithm. Neural Networks, 1996, pp. 149-182. Springer-Verlag. Google Scholar |
| [12] |
D.P. Mandic, A generalized normalized gradient descent algorithm, IEEE Signal Processing Letters, 11 (2004), 115-118. Google Scholar |
Figure 2.
Stages for the implementation of an AI computer program.
Figure 3.
Levels of mango maturity.
Figure 4.
Segmentation applied to separate the airplane from the sky (including clouds).
Figure 5.
Some colors and the corresponding RGB values.
Figure 6.
a), b), c), d) Color extraction images, e) SCILAB code.
Figure 7.
Model of an artificial neuron, named perceptron, with 3 input variables.
Figure 8.
An ANN with 3 layers.
Figure 9.
SCILAB code to calculate de ANN output.
Figure 11.
SCILAB program for ANN training.
Figure 12.
Mangoes selected for ANN training.
Figure 13.
Input and output variables for ANN training.
Figure 14.
Preliminary result of the classification of 30 mangoes.
Figure 15.
Green, medium, and mature mangoes for ANN training.
Figure 16.
Result with improved training using 6 mangoes.
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